Beyond the Tutorial: Building Agents with LlamaIndex & Qdrant

Are you ready to move beyond the basics and build advanced agents that can manage complex, real-world queries?

Watch this webinar recording on how to harness the power of LlamaIndex and Qdrant to create next-generation intelligent systems.

Learning objectives:

• Implement a RAG-enabled agent using LlamaIndex and Qdrant, expanding beyond traditional RAG applications by building an intelligent system capable of handling complex queries across multiple data modalities.
• Learn how to integrate LlamaIndex with Qdrant to leverage its vector database capabilities for enhanced search and retrieval performance in a RAG system.

Speakers

This webinar is ideal for data scientists, AI engineers, software developers, and tech enthusiasts who are looking to deepen their understanding of AI-driven agent systems and improve their implementation skills.

LlamaIndex is a powerful tool for managing and querying large datasets, offering an intuitive interface for integrating machine learning models. It simplifies the process of building retrieval-augmented generation (RAG) systems, enabling developers to create more sophisticated and responsive agents.

Qdrant is a high-performance vector database designed for handling large-scale, multidimensional data. With its ability to perform efficient similarity searches, Qdrant enhances the capabilities of RAG systems by optimizing search and retrieval processes, making it an indispensable component in your AI toolkit.